Novel Systems Modeling Methodology in Comparative Microbial Metabolomics: Identifying Key Enzymes and Metabolites Implicated in Autism Spectrum Disorders
Date
2015-04-22
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MDPI AG
Abstract
Autism spectrum disorders are a group of mental illnesses highly correlated with
gastrointestinal dysfunction. Recent studies have shown that there may be one or more
microbial “fingerprints” in terms of the composition characterizing individuals with autism,
which could be used for diagnostic purposes. This paper proposes a computational approach
whereby metagenomes characteristic of “healthy” and autistic individuals are artificially
constructed via genomic information, analyzed for the enzymes coded within, and then these
enzymes are compared in detail. This is a text mining application. A custom-designed online
application was built and used for the comparative metabolomics study and made publically
available. Several of the enzyme-catalyzing reactions involved with the amino acid glutamate
were curiously missing from the “autism” microbiome and were coded within almost
every organism included in the “control” microbiome. Interestingly, there exists a leading
hypothesis regarding autism and glutamate involving a neurological excitation/inhibition
imbalance; but the association with this study is unclear. The results included data on the
transsulfuration and transmethylation pathways, involved with oxidative stress, also of
importance to autism. The results from this study are in alignment with leading hypotheses
in the field, which is impressive, considering the purely in silico nature of this study. The
present study provides new insight into the complex metabolic interactions underlying
autism, and this novel methodology has potential to be useful for developing new
hypotheses. However, limitations include sparse genome data availability and conflicting literature experimental data. We believe our software tool and methodology has potential
for having great utility as data become more available, comprehensive and reliable.
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Int. J. Mol. Sci. 2015, 16, 8949-8967; doi:10.3390/ijms16048949